Field value search drill down

ABSTRACT

In embodiments of field value search drill down, a search system exposes a search interface that displays one or more events returned as a search result set. A field-value pair can be emphasized in the field-value pairs of an event displayed in the search interface, and a menu is displayed with search options that are selectable to operate on the emphasized field-value pair of the event. The menu includes the search options to add search criteria of the emphasized field-value pair to a search command in a search bar of the search interface, exclude the search criteria of the emphasized field-value pair from a search, or create a new data search based on the emphasized field-value pair. A selection of one of the search options in the menu can be received, and the search command in the search bar is updated based on the search option that is selected.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.14/526,406 filed Oct. 28, 2014 and titled “Filed Value Search DrillDown.” U.S. patent application Ser. No. 14/526,406 claims priority toU.S. Provisional Patent Application Nos. 62/059,988, 62/059,989,62/059,993, 62/059,994, 62/059,998, and 62/060,001 all filed Oct. 5,2014. U.S. patent application Ser. No. 14/526,406 also claims priorityto U.S. Provisional Patent Application Nos. 62/060,545, 62/060,551,62/060,560, and 62/060,567 all filed Oct. 6, 2014. The contents of eachof the foregoing applications are incorporated by reference herein intheir entirety.

BACKGROUND

Data analysts for many businesses face the challenge of making sense ofand finding patterns in the increasingly large amounts of data in themany types and formats that such businesses generate and collect. Forexample, accessing computer networks and transmitting electroniccommunications across the networks generates massive amounts of data,including such types of data as machine data and Web logs. Identifyingpatterns in this data, once thought relatively useless, has proven to beof great value to the businesses. In some instances, pattern analysiscan indicate which patterns are normal and which ones are unusual. Forexample, detecting unusual patterns can allow a computer system managerto investigate the circumstances and determine whether a computer systemsecurity threat exists.

Additionally, analysis of the data allows businesses to understand howtheir employees, potential consumers, and/or Web visitors use thecompany's online resources. Such analysis can provide businesses withoperational intelligence, business intelligence, and an ability tobetter manage their IT resources. For instance, such analysis may enablea business to better retain customers, meet customer needs, or improvethe efficiency of the company's IT resources. Despite the value that onecan derive from the underlying data described, making sense of this datato realize that value takes effort. In particular, patterns inunderlying data may be difficult to identify or understand whenanalyzing specific behaviors in isolation, often resulting in thefailure of a data analyst to notice valuable correlations in the datafrom which a business can draw strategic insight.

SUMMARY

This Summary introduces features and concepts of field value searchdrill down, which is further described below in the Detailed Descriptionand/or shown in the Figures. This Summary should not be considered todescribe essential features of the claimed subject matter, nor used todetermine or limit the scope of the claimed subject matter.

Field value search drill down is described. In embodiments, a searchsystem exposes a search interface that displays one or more eventsreturned as a search result set. A field-value pair can be emphasized inthe field-value pairs of an event displayed in the search interface, anda menu is displayed with search options that are selectable to operateon the emphasized field-value pair of the event. The menu includes thesearch options to add search criteria of the emphasized field-value pairto a search command in a search bar of the search interface, exclude thesearch criteria of the emphasized field-value pair from a search, orcreate a new data search based on the emphasized field-value pair. Aselection of one of the search options in the menu can be received, andthe search command in the search bar is updated based on the searchoption that is selected for the emphasized field-value pair.

In implementations, the search interface is an event field-pickerinterface that displays a listing of multiple field-value pairs of theevent. The field-value pair in the event can be highlighted responsiveto detection of an input pointer over the field-value pair. An inputassociated with the emphasized field-value pair can be received, such aswhen initiated by a user in the search interface, and the menu of thesearch options is displayed proximate the emphasized field-value pair inthe search interface. For example, the menu may pop-up or drop-down justbelow the emphasized field-value pair. A received input that isassociated with the emphasized field-value pair initiates a display ofthe menu with the search options that are selectable to operate on theemphasized field-value pair. The search options that are displayed inthe menu include an add to search option, an exclude from search option,and a new search option. The menu also includes a first statisticalevent count that indicates a number of multiple events that include theemphasized field-value pair, and the menu includes a second statisticalevent count that indicates a number of multiple events that exclude theemphasized field-value pair.

In embodiments, the search system can receive a selection of the searchoption to add search criteria of the emphasized field-value pair to adata search, and update the search command in the search bar to includethe search criteria of the emphasized field-value pair. The searchsystem can then perform the data search based on the updated searchcommand to determine additional events that each include the searchcriteria of the emphasized field-value pair, and display the additionalevents that each include the emphasized field-value pair in the searchinterface. Alternatively, the search system can receive a selection ofthe search option to exclude search criteria of the emphasizedfield-value pair from a data search, and update the search command inthe search bar to exclude the search criteria of the emphasizedfield-value pair. The search system can then perform the data searchbased on the updated search command to determine additional events thatdo not include the search criteria of the emphasized field-value pair,and display the additional events that do not include the emphasizedfield-value pair. Alternatively, the search system can receive aselection of the search option to create a new data search based on theemphasized field-value pair, and update the search command in the searchbar to replace the search command with search criteria of the emphasizedfield-value pair. The search system can then perform the new data searchbased on the updated search command to determine additional events thatinclude the search criteria of the emphasized field-value pair, anddisplay the additional events that include the emphasized field-valuepair.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of field value search drill down are described withreference to the following Figures. The same numbers may be usedthroughout to reference like features and components that are shown inthe Figures:

FIG. 1 illustrates a block diagram of an event-processing system inaccordance with the disclosed implementations of field value searchdrill down.

FIG. 2 illustrates a flowchart of how indexers process, index, and storedata received from forwarders in accordance with the disclosedimplementations.

FIG. 3 illustrates a flowchart of how a search head and indexers performa search query in accordance with the disclosed implementations.

FIG. 4 illustrates a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withthe disclosed implementations.

FIG. 5 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with the disclosedimplementations.

FIG. 6A illustrates a search screen in accordance with the disclosedimplementations.

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed implementations.

FIG. 7A illustrates a key indicators view in accordance with thedisclosed implementations.

FIG. 7B illustrates an incident review dashboard in accordance with thedisclosed implementations.

FIG. 7C illustrates a proactive monitoring tree in accordance with thedisclosed implementations.

FIG. 7D illustrates a screen displaying both log data and performancedata in accordance with the disclosed implementations.

FIGS. 8A-8G illustrate examples of a search interface in accordance withthe disclosed implementations.

FIG. 9A illustrates an example of a search interface in accordance withthe disclosed implementations.

FIG. 9B illustrates an example of an extract fields interface inaccordance with the disclosed implementations.

FIGS. 10A and 10B illustrate examples of a search interface inaccordance with the disclosed implementations.

FIGS. 11A-11D illustrate example method(s) of event segment search drilldown in accordance with one or more embodiments.

FIGS. 12A-12D illustrate example method(s) of field value search drilldown in accordance with one or more embodiments.

FIG. 13 illustrates an example system with an example device that canimplement embodiments of field value search drill down.

DETAILED DESCRIPTION

Embodiments of event segment search drill down and field value searchdrill down are described and can be implemented to facilitateuser-initiated search options when performing data searches in searchinterfaces that include events, highlighted segments in event raw dataof the events, values of field-value pairs in the events, and taggedfield-value pairs in the events. Although described in the context of anevent segment that is highlighted or otherwise visually emphasized inevent raw data of a displayed event, the techniques described herein canbe implemented and applied to any text selection, alphanumericselection, or searched text and/or alphanumeric string.

In embodiments, a segment in the event raw data of an event can behighlighted (or otherwise emphasized) and a contextual search menu isdisplayed with search options that are selectable to operate on thehighlighted segment. Similarly, a field-value pair in an event can beemphasized (e.g., highlighted or any other type of visual emphasis) anda field value contextual menu is displayed with search options that areselectable to operate on the emphasized field-value pair.

The contextual search menu and the field value contextual menu includessearch options, such as to add the highlighted segment as a new keywordto a search command in a search bar of the search interface, add thekeyword that represents the highlighted segment to the search command toexclude the highlighted segment from a search, or create a new datasearch based on the keyword that represents the highlighted segment.Similarly, the search options include an option to add search criteriaof the emphasized field-value pair to the search command in the searchbar of the search interface, add the search criteria of the emphasizedfield-value pair to the search command as the search criteria excludedfrom events that do not include the emphasized field-value pair, orcreate a new data search based on the emphasized field-value pair, wherethe search criteria of the emphasized field-value pair replaces thesearch command in the search bar. The user can select one of the searchoptions in the contextual search menu or field value contextual menu,and the search command in the search bar of the search interface isupdated based on the search option that is selected for the highlightedsegment or emphasized field-value pair.

Example Environment

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, Calif., to store and process performance data. The SPLUNK®ENTERPRISE system is the leading platform for providing real-timeoperational intelligence that enables organizations to collect, index,and harness machine-generated data from various websites, applications,servers, networks, and mobile devices that power their businesses. TheSPLUNK® ENTERPRISE system is particularly useful for analyzingunstructured performance data, which is commonly found in system logfiles. Although many of the techniques described herein are explainedwith reference to the SPLUNK® ENTERPRISE system, the techniques are alsoapplicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” in which each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” in which time series data includes a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, inwhich specific data items with specific data formats reside atpredefined locations in the data. For example, structured data caninclude data items stored in fields in a database table. In contrast,unstructured data does not have a predefined format. This means thatunstructured data can include various data items having different datatypes that can reside at different locations. For example, when the datasource is an operating system log, an event can include one or morelines from the operating system log containing raw data that includesdifferent types of performance and diagnostic information associatedwith a specific point in time.

Examples of data sources from which an event may be derived include, butare not limited to web servers, application servers, databases,firewalls, routers, operating systems, and software applications thatexecute on computer systems, mobile devices, and sensors. The datagenerated by such data sources can be produced in various formsincluding, for example and without limitation, server log files,activity log files, configuration files, messages, network packet data,performance measurements and sensor measurements. An event typicallyincludes a timestamp that may be derived from the raw data in the event,or may be determined through interpolation between temporally proximateevents having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, in which theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly” as desired(e.g., at search time), rather than at ingestion time of the data as intraditional database systems. Because the schema is not applied to eventdata until it is desired (e.g., at search time), it is referred to as a“late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction ruleincludes a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques. Also, a number of “defaultfields” that specify metadata about the events, rather than data in theevents themselves, can be created automatically. For example, suchdefault fields can specify: a timestamp for the event data; a host fromwhich the event data originated; a source of the event data; and asource type for the event data. These default fields may be determinedautomatically when the events are created, indexed, or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

Data Server System

FIG. 1 illustrates a block diagram of an example event-processing system100, similar to the SPLUNK® ENTERPRISE system, and in which embodimentsof event segment search drill down can be implemented. The exampleevent-processing system 100 includes one or more forwarders 101 thatcollect data obtained from a variety of different data sources 105, andone or more indexers 102 that store, process, and/or perform operationson this data, in which each indexer operates on data contained in aspecific data store 103. A search head 104 may also be provided thatrepresents functionality to obtain and process search requests fromclients and provide results of the search back to the clients,additional details of which are discussed in relation to FIGS. 3 and 4.The forwarders 101, indexers 102, and/or search head 104 may beconfigured as separate computer systems in a data center, oralternatively may be configured as separate processes implemented viaone or more individual computer systems. Data that is collected via theforwarders 101 may be obtained from a variety of different data sources105.

As further illustrated, the search head 104 may interact with a clientapplication module 106 associated with a client device, such as toobtain search queries and supply search results or other suitable databack to the client application module 106 that is effective to enablethe client application module 106 to form search user interfaces 108through which different views of the data may be exposed. Variousexamples and details regarding search interfaces 108, client applicationmodules 106, search queries, and operation of the various componentsillustrated in FIG. 1 are discussed throughout this document.

During operation, the forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers. The forwarders 101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders nextdetermine which of the indexers 102 will receive each data item and thenforward the data items to the determined indexers 102. Note thatdistributing data across the different indexers 102 facilitates parallelprocessing. This parallel processing can take place at data ingestiontime, because multiple indexers can process the incoming data inparallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

The example event-processing system 100 and the processes describedbelow with respect to FIGS. 1-5 are further described in “ExploringSplunk Search Processing Language (SPL) Primer and Cookbook” by DavidCarasso, CITO Research, 2012, and in “Optimizing Data Analysis With aSemi-Structured Time Series Database” by Ledion Bitincka, ArchanaGanapathi, Stephen Sorkin, and Steve Zhang, SLAML, 2010, each of whichis hereby incorporated herein by reference in its entirety for allpurposes.

Data Ingestion

FIG. 2 illustrates a flowchart 200 of how an indexer processes, indexes,and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, in which the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, in which the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2” as a field-value pair.

Finally, the indexer stores the events in a data store at block 208,where a timestamp can be stored with each event to facilitate searchingfor events based on a time range. In some cases, the stored events areorganized into a plurality of buckets, where each bucket stores eventsassociated with a specific time range. This not only improves time-basedsearches, but it also allows events with recent timestamps that may havea higher likelihood of being accessed to be stored in faster memory tofacilitate faster retrieval. For example, a bucket containing the mostrecent events can be stored as flash memory instead of on a hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,in which each indexer returns partial responses for a subset of eventsto a search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query. Moreover, events and buckets canalso be replicated across different indexers and data stores tofacilitate high availability and disaster recovery as is described inU.S. patent application Ser. No. 14/266,812 filed on 30 Apr. 2014, andin U.S. patent application Ser. No. 14/266,817 also filed on 30 Apr.2014.

Query Processing

FIG. 3 illustrates a flowchart 300 of how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient (e.g., a client computing device) at block 301. Next, at block302, the search head analyzes the search query to determine whatportions can be delegated to indexers and what portions need to beexecuted locally by the search head. At block 303, the search headdistributes the determined portions of the query to the indexers. Notethat commands that operate on single events can be trivially delegatedto the indexers, while commands that involve events from multipleindexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending on what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

Field Extraction

FIG. 4 illustrates a block diagram 400 of how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 402 is received at a queryprocessor 404. The query processor 404 includes various mechanisms forprocessing a query, where these mechanisms can reside in a search head104 and/or an indexer 102. Note that the exemplary search query 402illustrated in FIG. 4 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. The SPLis a pipelined search language in which a set of inputs is operated onby a first command in a command line, and then a subsequent commandfollowing the pipe symbol “I” operates on the results produced by thefirst command, and so on for additional commands. Search query 402 canalso be expressed in other query languages, such as the Structured QueryLanguage (“SQL”) or any suitable query language.

Upon receiving the search query 402, the query processor 404 identifiesthat the search query 402 includes two fields, “IP” and “target.” Thequery processor 404 also determines that the values for the “IP” and“target” fields have not already been extracted from events in a datastore 414, and consequently determines that the query processor 404needs to use extraction rules to extract values for the fields. Hence,the query processor 404 performs a lookup for the extraction rules in arule base 406, in which rule base 406 maps field names to correspondingextraction rules and obtains extraction rules 408 and 409, whereextraction rule 408 specifies how to extract a value for the “IP” fieldfrom an event, and extraction rule 409 specifies how to extract a valuefor the “target” field from an event.

As is illustrated in FIG. 4, the extraction rules 408 and 409 caninclude regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, the query processor 404 sends the extraction rules 408 and 409 toa field extractor 412, which applies the extraction rules 408 and 409 toevents 416-418 in the data store 414. Note that the data store 414 caninclude one or more data stores, and the extraction rules 408 and 409can be applied to large numbers of events in the data store 414, and arenot meant to be limited to the three events 416-418 illustrated in FIG.4. Moreover, the query processor 404 can instruct the field extractor412 to apply the extraction rules to all of the events in the data store414, or to a subset of the events that have been filtered based on somecriteria.

Next, the field extractor 412 applies the extraction rule 408 for thefirst command “Search IP=“10*” to events in the data store 414,including the events 416-418. The extraction rule 408 is used to extractvalues for the IP address field from events in the data store 414 bylooking for a pattern of one or more digits, followed by a period,followed again by one or more digits, followed by another period,followed again by one or more digits, followed by another period, andfollowed again by one or more digits. Next, the field extractor 412returns field values 420 to the query processor 404, which uses thecriterion IP=″10*” to look for IP addresses that start with “10”. Notethat events 416 and 417 match this criterion, but event 418 does not, sothe result set for the first command is events 416 and 417.

The query processor 404 then sends the events 416 and 417 to the nextcommand “stats count target.” To process this command, the queryprocessor 404 causes the field extractor 412 to apply the extractionrule 409 to the events 416 and 417. The extraction rule 409 is used toextract values for the target field for the events 416 and 417 byskipping the first four commas in the events, and then extracting all ofthe following characters until a comma or period is reached. Next, thefield extractor 412 returns field values 421 to the query processor 404,which executes the command “stats count target” to count the number ofunique values contained in the target fields, which in this exampleproduces the value “2” that is returned as a final result 422 for thequery.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, the queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

Example Search Screen

FIG. 6A illustrates an example of a search screen 600 in accordance withthe disclosed embodiments. The search screen 600 includes a search bar602 that accepts user input in the form of a search string. It alsoincludes a date time range picker 612 that enables the user to specify adate and/or time range for the search. For “historical searches” theuser can select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday,” or “last week.” For “real-timesearches,” the user can select the size of a preceding time window tosearch for real-time events. The search screen 600 also initiallydisplays a “data summary” dialog 610 as is illustrated in FIG. 6B thatenables the user to select different sources for the event data, such asby selecting specific hosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, where the search results tabs604 include: an “Events” tab that displays various information aboutevents returned by the search; a “Patterns” tab that can be selected todisplay various patterns about the events returned by the search; a“Statistics” tab that displays statistics about the search results andevents; and a “Visualization” tab that displays various visualizationsof the search results. The “Events” tab illustrated in FIG. 6A displaysa timeline graph 605 that graphically illustrates the number of eventsthat occurred in one-hour intervals over the selected time range. Italso displays an events list 608 that enables a user to view the rawdata in each of the returned events. It additionally displays a fieldssidebar 606 that includes statistics about occurrences of specificfields in the returned events, including “selected fields” that arepre-selected by the user, and “interesting fields” that areautomatically selected by the system based on pre-specified criteria.

Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 5 illustrates an example500 of how a search query 501 received from a client at search head 104can split into two phases, including: (1) a “map phase” comprisingsubtasks 502 (e.g., data retrieval or simple filtering) that may beperformed in parallel and are “mapped” to indexers 102 for execution,and (2) a “reduce phase” comprising a merging operation 503 to beexecuted by the search head 104 when the results are ultimatelycollected from the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 3, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

Keyword Index

As described above with reference to the flow charts 200 and 300 shownin respective FIGS. 2 and 3, the event-processing system 100 canconstruct and maintain one or more keyword indices to facilitate rapidlyidentifying events containing specific keywords. This can greatly speedup the processing of queries involving specific keywords. As mentionedabove, to build a keyword index, an indexer first identifies a set ofkeywords. Then, the indexer includes the identified keywords in anindex, which associates each stored keyword with references to eventscontaining that keyword, or to locations within events where thatkeyword is located. When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100make use of a high-performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an entry in a summarization table can keep track of occurrences of thevalue “94107” in a “ZIP code” field of a set of events, where the entryincludes references to all of the events that contain the value “94107”in the ZIP code field. This enables the system to quickly processqueries that seek to determine how many events have a particular valuefor a particular field, because the system can examine the entry in thesummarization table to count instances of the specific value in thefield without having to go through the individual events or doextractions at search time. Also, if the system needs to process each ofthe events that have a specific field-value combination, the system canuse the references in the summarization table entry to directly accessthe events to extract further information without having to search eachof the events to find the specific field-value combination at searchtime.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, where a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, in which theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover each of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods. If reports canbe accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only the events withinthe time period that meet the specified criteria. Similarly, if thequery seeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated.

Alternatively, if the system stores events in buckets covering specifictime ranges, then the summaries can be generated on a bucket-by-bucketbasis. Note that producing intermediate summaries can save the workinvolved in re-running the query for previous time periods, so only thenewer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, issued on Nov. 19, 2013, and in U.S.Pat. No. 8,412,696, issued on Apr. 2, 2011.

Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that make it easy for developers to createapplications to provide additional capabilities. One such application isthe SPLUNK® APP FOR ENTERPRISE SECURITY, which performs monitoring andalerting operations, and includes analytics to facilitate identifyingboth known and unknown security threats based on large volumes of datastored by the SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, where the extracted datais typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. patentapplication Ser. Nos. 13/956,252, and 13/956,262. Security-relatedinformation can also include endpoint information, such as malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices, and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 7A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased bysixty-three (63) during the preceding interval. The key indicators view700 additionally displays a histogram panel 704 that displays ahistogram of notable events organized by urgency values, and a histogrampanel 705 of notable events organized by time intervals. This keyindicators view is described in further detail in pending U.S. patentapplication Ser. No. 13/956,338 filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 7B illustrates an example of an incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of each of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, or critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent. The incident review dashboard is described further on-line (e.g.,at an HTTP:// site),“docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference. Also, see “vSphere Monitoring and Performance,” Update 1,vSphere 5.5, EN-001357-00 on-line (e.g., at an HTTP:// site),“pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.”

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas. The SPLUNK® APP FOR VMWARE® additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Exemplary node-expansionoperations are illustrated in FIG. 7C, where nodes 733 and 734 areselectively expanded. Note that the nodes 731-739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state, or anunknown/off-line state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. patent application Ser. No. 14/235,490 filed on15 Apr. 2014, which is hereby incorporated herein by reference for allpossible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data, and associated performance metrics, for theselected time range. For example, the interface screen illustrated inFIG. 7D displays a listing of recent “tasks and events” and a listing ofrecent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316 filed on 29 Jan. 2014, which is hereby incorporatedherein by reference for all possible purposes.

Event Segment Search Drill Down

FIG. 8A illustrates an example of a search interface 800 displayed as agraphical user interface in accordance with the disclosed embodimentsfor event segment search drill down. The search interface 800 includes asearch bar 802 that displays a search command 804, which is“sourcetype=access_combined” in this example. The search interface 800also displays events 806 that are each correlated by a date and time808. As described previously, the events 806 are a result set ofperforming the search command 804 that is currently displayed in thesearch bar 802, and only a subset of the events are shown in the searchinterface. A user can scroll the list of events 806 in the searchinterface 800 to view additional events of the search result set thatare not displayed.

An event 810 (e.g., the first displayed event in the list of events 806)generally incudes displayed event information, depending on a selectedevent view from which a user can select a format to display some or allof the event information for each of the events 806 in the searchinterface. In the example search interface 800, the events 806 aredisplayed in a list view, in which case the displayed event informationfor event 810 includes event raw data 812 displayed in an upper portionof the event display area, and includes field-value pairs 814 displayedin a lower portion of the event display area. The field-value pairs 814correlate to selected fields 820 that are also displayed in a fieldssidebar 818. In this example, each of the events 806 include“host=jmiller-mbpr15.sv.splunk.com” as a field-value pair 816. Thesearch interface 800 includes the fields sidebar 818, which displays theselected fields 820 that are also displayed as the fields 816 for eachof the events 806, and the fields sidebar 818 also includes otherinteresting fields 822.

In this example search interface 800, a user may highlight any of thesegments (e.g., terms or a combination of terms) in the event raw data812, such as “Mozilla/5.0” shown as the highlighted segment 824 in theevent raw data 812 of the event 810. In implementations, the SPLUNK®ENTERPRISE system includes a segmenter that is implemented to analyzethe event raw data as a data string and determine which of the terms orcombinations of terms are the contextually interesting segments thatusers (e.g., the data analysts) would most likely be interested insearching on or otherwise looking into. The segmenter identifies thesegments (also referred to as terms or keywords) in the event raw data,and when a user moves a mouse pointer or other input device over asegment that the segmenter has identified, then the segment ishighlighted in the display of the search interface.

In implementations, a segment may be highlighted or otherwise emphasizedwhen a pointer that is displayed in the search interface 800 moves overa particular segment. This feature is also referred to as highlight withrollover (e.g., detected when a pointer moves over a segment). Forexample, a user may move a mouse pointer over the “Mozilla/5.0” segment,which is then displayed as the highlighted segment 824. Alternatively, auser can highlight a segment in the event raw data 812 by initiating aselection of the segment, such as with a computer mouse, stylus, orother input device. A highlighted segment can then be selected inresponse to a user input, such as with a mouse click or touch input toselect a particular segment.

The search interface 800 also includes an event field-picker toggle 826that a user can select and initiate a transition to an alternate view ofthe search interface for the displayed event 810, which is shown as anevent field-picker interface 828 and further described with reference toFIG. 8B. The event-limited field picker interface enables user selectionof fields associated with individual events to display in the view ofthe events in the search interface.

FIG. 8B illustrates an example of the event field-picker interface 828,as an alternate view of the search interface 800 described withreference to FIG. 8A. A user can transition from the displayed view ofthe search interface 800 (shown in FIG. 8A) to the displayed view of theevent field-picker interface 828 (shown in FIG. 8B) by selecting theevent field-picker toggle 826 that corresponds to the displayed event810. In this example, the event field-picker interface 828 includes alisting 830 of the field 832 and value 834 pairs in the event 810. Theevent field-picker interface 828 is further described with reference toFIG. 10B in accordance with the disclosed embodiments for field valuesearch drill down.

FIG. 8C further illustrates the example of the search interface 800described with reference to FIG. 8A in accordance with the disclosedembodiments for event segment search drill down. In this example displayof the search interface 800, a user has initiated the segment 824 beinghighlighted, such as with a mouse pointer moved over the “Mozilla/5.0”segment, which is displayed as the highlighted segment 824 in the eventraw data 812. Additionally, the user has selected the highlightedsegment 824, such as with a mouse click or touch input, and a contextualsearch menu 836 is displayed responsive to the user input. Inimplementations, the contextual search menu 836 is displayed proximatethe highlighted segment 824 in the search interface 800, such as apop-up or drop-down menu just below the highlighted segment. Althoughdescribed in the context of an event segment that is highlighted inevent raw data of a displayed event, the techniques described herein canbe implemented and applied to any text selection, alphanumericselection, or searched text and/or alphanumeric string.

The contextual search menu 836 includes search options 838 that areselectable to operate on the highlighted segment 824 in the event rawdata 812 of the displayed event 810. For example, the search options 838displayed in the contextual search menu 836 include: an option “Add tosearch” 840 that a user can select to add the highlighted segment 824 asa new keyword to the search command 804 in the search bar 802; an option“Exclude from search” 842 that the user can select to exclude thekeyword that represents the highlighted segment 824 from searches; andan option “New search” 844 that the user can select to create a new datasearch based on the highlighted segment 824 (e.g., replacing the searchcommand 804 in the search bar 802 with the keyword that represents thehighlighted segment 824). A user selection of one of the search options838 in the contextual search menu 836 can be received, and the searchcommand 804 in the search bar 802 is updated based on the search optionthat is selected for the highlighted segment. In this example, thecontextual search menu 836 also includes selectable interface links 846that are each associated with a corresponding search option 838 in thesearch menu. A selectable interface link 846 for an associated searchoption can be selected by a user to initiate a new search interface.

FIG. 8D further illustrates the example of the search interface 800described with reference to FIGS. 8A and 8C in accordance with thedisclosed embodiments for event segment search drill down. In thisexample display of the search interface 800, a user has selected theoption “Add to search” 840 from the contextual search menu 836 (shown inFIG. 8C) to add the highlighted segment 824 as a new keyword to a datasearch, and update the search command 804 in the search bar 802 toinclude the keyword that represents the highlighted segment, which isshown as “Mozilla/5.0” added to the search command 804. The searchsystem can then perform the data search based on the updated searchcommand 804 to determine the multiple events 806 that each include thekeyword that represents the highlighted segment, and display an updatedsearch result set of the events 806 that each include the highlightedsegment in the search interface 800. For example, each of the displayedevents 806 in the search interface 800 include the highlighted“Mozilla/5.0” segment, as shown generally at 848. Note that each of thedisplayed events 806 in the search interface 800 also include the restof the search command 804 (e.g., “sourcetype=access-_combined”) as afield-value pair 816.

FIG. 8E further illustrates the example of the search interface 800described with reference to FIGS. 8A, 8C, and 8D, in which the multipleevents 806 that each include the highlighted “Mozilla/5.0” segment 824are displayed, as shown generally at 848. In this example display of thesearch interface 800, a user has selected the highlighted segment 824(or any of the similar highlighted segments 848), such as with a mouseclick or touch input, and an additional search menu 850 is displayedresponsive to the user input. In implementations, the additional searchmenu 850 is displayed proximate the highlighted segment 824 in thesearch interface 800, such as a pop-up or drop-down menu just below thehighlighted segment.

The additional search menu 850 includes search options 852 that areselectable to operate on the highlighted segment 824 in the event rawdata 812 of the displayed event 810. For example, the search options 852displayed in the additional search menu 850 include: an option “Removefrom search” 854 that a user can select to remove the keyword thatrepresents the highlighted segment 824 from the search command 804 inthe search bar 802; and includes an option “New search” 856 that theuser can select to create a new data search based on the highlightedsegment 824 (e.g., replacing the search command 804 with the keywordthat represents the highlighted segment 824). A user selection of one ofthe search options 852 in the additional search menu 850 can bereceived, and the search command 804 in the search bar 802 is updatedbased on the search option that is selected for the highlighted segment.In this example, the additional search menu 850 also includes selectableinterface links 858 that are each associated with a corresponding searchoption 852 in the additional search menu. A selectable interface link858 for an associated search option can be selected by a user toinitiate a new search interface.

FIG. 8F further illustrates the example of the search interface 800described with reference to FIGS. 8A and 8C in accordance with thedisclosed embodiments for event segment search drill down. In thisexample display of the search interface 800, a user has selected theoption “Exclude from search” 842 from the contextual search menu 836(shown in FIG. 8C) to exclude the keyword that represents thehighlighted segment 824 from a data search, and update the searchcommand 804 in the search bar 802 to indicate that the keyword thatrepresents the highlighted segment is excluded, which is shown as thekeywords “NOT “Mozilla/5.0”” added to the search command 804. The searchsystem can then perform the data search based on the updated searchcommand 804 to determine the multiple events 806 that do not include thehighlighted segment, and display an updated search result set of theevents 806 that do not include the highlighted segment in the searchinterface 800. For example, each of the displayed events 806 in thesearch interface 800 do not include the highlighted “Mozilla/5.0”segment, but still do include the rest of the search command 804 (e.g.,“sourcetype=access_combined”) as a field-value pair 816.

FIG. 8G further illustrates the example of the search interface 800described with reference to FIGS. 8A and 8C in accordance with thedisclosed embodiments for event segment search drill down. In thisexample display of the search interface 800, a user has selected theoption “New search” 844 from the search contextual menu 836 (shown inFIG. 8C) to create a new data search based on the highlighted segment824, and update the search command 804 in the search bar 802 to includeonly the keyword that represents the highlighted segment, which is shownas “Mozilla/5.0” as the keyword in the search command 804. The searchsystem can then perform the new data search based on the updated searchcommand 804 to determine the multiple events 806 that each include thekeyword that represents the highlighted segment, and display an updatedsearch result set of the events 806 that each include the highlightedsegment in the search interface 800. For example, each of the displayedevents 806 in the search interface 800 include the highlighted“Mozilla/5.0” segment, as shown generally at 848.

FIG. 9A further illustrates the example of the search interface 800described with reference to FIG. 8A in accordance with the disclosedembodiments for event segment search drill down. In this example displayof the search interface 800, a user has initiated the segment 824 beinghighlighted, such as with a mouse pointer moved over the “Mozilla/5.0”segment, which is displayed as the highlighted segment 824 in the eventraw data 812. Additionally, the user has selected the highlightedsegment 824, such as with a mouse click or touch input, and a contextualsearch menu 900 is displayed responsive to the user input. Inimplementations, the contextual search menu 900 is displayed proximatethe highlighted segment 824 in the search interface 800, such as apop-up or drop-down menu just below the highlighted segment.

The contextual search menu 900 includes search options 902 that areselectable to operate on the highlighted segment 824 in the event rawdata 812 of the displayed event 810. For example, the search options 902displayed in the contextual search menu 900 include: an option “Add tosearch” 904 that a user can select to add the highlighted segment 824 asa new keyword to the search command 804 in the search bar 802; an option“Exclude from search” 906 that a user can select to exclude the keywordthat represents the highlighted segment 824 from searches; an option“New search” 908 that a user can select to create a new data searchbased on the highlighted segment 824 (e.g., replacing the search command804 with the keyword that represents the highlighted segment 824); andan option “Field Extraction” 910 that a user can select to initiate anextract fields interface 912 shown in FIG. 9B that is usable to define acustom event field for an event 806.

A custom event field is a field that has been extracted from an event,such as by using a regex rule or other techniques. The field extractionprocess creates a regex that is used to extract fields from an event andthe fields that are extracted are custom event fields. An extract fieldsmenu 916 includes an entry “Field Name” 918 that a user can enter toname a field that is to be extracted. The extract fields menu 916 alsoincludes selectable options to “Extract” 920, “Require” 922, and “AddExtraction” 924. The user can select the option “Extract” 920 to createa field extraction (or regex) for the text selected. For example, if anevent includes the text “foobar=baz” and the user clicks to select thetext “baz”, then selects the option “Extract”, enters the “Field Name”as “foobarField”, and clicks on the option “Add Extraction”, then theuser has created a field extraction that has a regex that determineswhether an event has the text “foobar=[any value]”. If an event includesthis text, then a field will be created for this event with the fieldname “foobarField” and the value [any value]. If the user selects theoption “Require” 922, then the selected text is not extracted, butrather, the events that include the selected text are identified for theuser while setting up the field extraction. The option to “AddExtraction” 924 then saves the regex rule for the field extraction.

A user selection of one of the search options 902 in the contextualsearch menu 900 can be received, and the search command 804 in thesearch bar 802 is updated based on the search option that is selectedfor the highlighted segment. In this example, the contextual search menu900 also includes selectable interface links 926 that are eachassociated with a corresponding search option 902 in the contextualsearch menu. A selectable interface link 926 for an associated searchoption can be selected by a user to initiate a new search interface.

Field Value Search Drill Down

FIG. 10A further illustrates an example of the search interface 800described with reference to FIG. 8A in accordance with the disclosedembodiments for field value search drill down. In this example displayof the search interface 800, a user has initiated a field-value pair1000 of a field-value pair 814 being emphasized (e.g., highlighted),such as with a mouse pointer moved over the “access_combined.log” value,which is displayed as the emphasized field-value pair 1000 in thefield-value pairs of the event 810. Additionally, the user has selectedthe emphasized field-value pair 1000, such as with a mouse click ortouch input, and a field value contextual menu 1002 is displayedresponsive to the user input. In implementations, the field valuecontextual menu 1002 is displayed proximate the emphasized field-valuepair 1000 in the search interface 800, such as a pop-up or drop-downmenu just below the emphasized field-value pair. The describedimplementations of field value search drill down can also be applied totagged field-value pairs, where a tag 1003 identifies a specificfield-value pair 814 and any of the events 806 that have the taggedfield-value pair can be displayed with the associated tag. A tag 1003 isan alias that designates a field-value pair, and can be selected to addsearch criteria that represents the tag to the search command 804 tosearch for events that have the field-value pair designated by the tag.

The field value contextual menu 1002 includes search options 1004 thatare selectable to operate on the emphasized field-value pair 1000 in thedisplayed event 810. For example, the search options 1004 displayed inthe field value contextual menu 1002 include: an option “Add to search”1006 that a user can select to add search criteria of the emphasizedfield-value pair 1000 to the search command 804 in the search bar 802;an option “Exclude from search” 1008 that the user can select to addsearch criteria of the emphasized field-value pair 1000 to the searchcommand in the search bar as the search criteria excluded from eventsthat do not include the emphasized field-value pair; and an option “Newsearch” 1010 that the user can select to create a new data search basedon the emphasized field-value pair 1000 (e.g., replacing the searchcommand 804 with the search criteria of the emphasized field-value pair1000). A user selection of one of the search options 1004 in the fieldvalue contextual menu 1002 can be received, and the search command 804in the search bar 802 is updated based on the search option that isselected for the emphasized field-value pair.

In this example, the field value contextual menu 1002 includes astatistical event count 1012 that is associated with the option “Add tosearch” 1006, and that indicates a number of multiple events thatinclude the search criteria of the emphasized field-value pair 1000 ofthe field-value pairs 814. The field value contextual menu 1002 alsoincludes a statistical event count 1014 that is associated with theoption “Exclude from search” 1008, and that indicates a number ofmultiple events that exclude the search criteria of the emphasizedfield-value pair 1000. The field value contextual menu 1002 alsoincludes selectable interface links 1016 that are each associated with acorresponding search option 1004 in the field value contextual menu. Aselectable interface link 1016 for an associated search option can beselected by a user to initiate a new search interface.

A user may select the option “Add to search” 1006 from the field valuecontextual menu 1002 to add search criteria of the emphasizedfield-value pair 1000 to a data search, and the search command 804 inthe search bar 802 is updated to include the search criteria of theemphasized field-value pair (similar to the example of the highlightedsegment being added as a keyword to the search command as shown in FIG.8D). The search system can then perform the data search based on theupdated search command 804 to determine the multiple events 806 thateach include the search criteria of the emphasized field-value pair, anddisplay an updated search result set of the events 806 that each includethe emphasized field-value pair in the search interface 800.

Similar to the example of the additional search interface 850 shown anddescribed with reference to FIG. 8E, multiple events may include thesearch criteria of the emphasized field-value pair that has been addedto the search. A selection of the emphasized field-value pair in adisplayed event initiates the additional search menu 850, which includesthe option “Remove from search” 854 that a user can select to remove thesearch criteria of the emphasized field-value pair from the searchcommand 804 in the search bar 802; and includes the option “New search”856 that the user can select to create a new data search based on thesearch criteria of the emphasized field-value pair (e.g., replacing thesearch command 804 with the search criteria of the emphasizedfield-value pair).

Alternatively, a user may select the option “Exclude from search” 1008from the field value contextual menu 1002 to exclude the search criteriaof the emphasized field-value pair 1000 from a data search, and updatethe search command 804 in the search bar 802 to indicate that theemphasized field-value pair is excluded (similar to the example of thehighlighted segment shown following the “NOT” operator in the searchcommand as shown in FIG. 8F). The search system can then perform thedata search based on the updated search command 804 to determine themultiple events 806 that do not include the search criteria of theemphasized field-value pair, and display an updated search result set ofthe events 806 that do not include the emphasized field-value pair inthe search interface 800.

Alternatively, a user may select the option “New search” 1010 from thefield value contextual menu 1002 to create a new data search based onthe emphasized field-value pair 1002, and update the search command 804in the search bar 802 to replace the search command in the search barwith search criteria of the emphasized field-value pair (similar to theexample of the keyword that represents the highlighted segment added asthe only search command 804 in the search bar 802 as shown in FIG. 8G).The search system can then perform the new data search based on theupdated search command 804 to determine the multiple events 806 thateach include the search criteria of the emphasized field-value pair, anddisplay an updated search result set of the events 806 that each includethe emphasized field-value pair in the search interface 800.

FIG. 10B further illustrates an example of the event field-pickerinterface 828 described with reference to FIG. 8B in accordance with thedisclosed embodiments for field value search drill down. In this exampledisplay of the field-picker interface 828, a user has initiated afield-value pair 1018 of one of the field-value pairs (832, 834) beingemphasized (e.g., highlighted), such as with a mouse pointer moved overthe “splunkid_access” value, which is displayed as the emphasizedfield-value pair 1018 in the listing 830 of the field 832 and value 834pairs in the event 810. Additionally, the user has selected theemphasized field-value pair 1018, such as with a mouse click or touchinput, and a field value contextual menu 1020 is displayed responsive tothe user input. In implementations, the field value contextual menu 1020is displayed proximate the emphasized field-value pair 1018 in the eventfield-picker interface 828, such as a pop-up or drop-down menu justbelow the emphasized field-value pair. The described implementations offield value search drill down can also be applied to tagged field-valuepairs, where a tag identifies a specific field-value pair (832, 834) andany of the events 806 that have the tagged field-value pair can bedisplayed with the associated tag.

The field value contextual menu 1020 includes search options 1022 thatare selectable to operate on the emphasized field-value pair 1018 in thedisplayed event 810. For example, the search options 1022 displayed inthe field value contextual menu 1020 include: an option “Add to search”1024 that a user can select to add search criteria of the emphasizedfield-value pair 1018 to the search command 804 in the search bar 802;an option “Exclude from search” 1026 that a user can select to addsearch criteria of the emphasized field-value pair 1018 to the searchcommand in the search bar as the search criteria excluded from eventsthat do not include the emphasized field-value pair; and an option “Newsearch” 1028 that a user can select to create a new data search based onthe emphasized field-value pair 1018 (e.g., replacing the search command804 with the search criteria of the emphasized field-value pair 1018). Auser selection of one of the search options 1022 in the field valuecontextual menu 1020 can be received, and the search command 804 in thesearch bar 802 is updated based on the search option that is selectedfor the emphasized field-value pair.

In this example, the field value contextual menu 1020 includes astatistical event count 1030 that is associated with the option “Add tosearch” 1024, and that indicates a number of multiple events thatinclude the search criteria of the emphasized field-value pair 1018. Thefield value contextual menu 1020 also includes a statistical event count1032 that is associated with the option “Exclude from search” 1026, andthat indicates a number of multiple events that exclude the searchcriteria of the emphasized field-value pair 1018. The field valuecontextual menu 1020 also includes selectable interface links 1034 thatare each associated with a corresponding search option 1022 in the fieldvalue contextual menu. A selectable interface link 1034 for anassociated search option can be selected by a user to initiate a newsearch interface.

A user may select the option “Add to search” 1024 from the field valuecontextual menu 1020 to add search criteria of the emphasizedfield-value pair 1018 to a data search, and the search command 804 inthe search bar 802 is updated to include the search criteria of theemphasized field-value pair (similar to the example of the highlightedsegment being added as a keyword to the search command as shown in FIG.8D). The search system can then perform the data search based on theupdated search command 804 to determine additional events that eachinclude the search criteria of the emphasized field-value pair, anddisplay an updated search result set of the events that each include theemphasized field-value pair, such as in the search interface 800 thatlists the events 806.

Alternatively, a user may select the option “Exclude from search” 1026from the field value contextual menu 1020 to exclude the search criteriaof the emphasized field-value pair 1018 from a data search, and updatethe search command 804 in the search bar 802 to indicate that theemphasized field-value pair is excluded (similar to the example of thehighlighted segment shown following the “NOT” operator in the searchcommand as shown in FIG. 8F). The search system can then perform thedata search based on the updated search command 804 to determineadditional events that do not include the search criteria of theemphasized field-value pair, and display an updated search result set ofthe events that do not include the emphasized field-value pair, such asin the search interface 800 that lists the events 806.

Alternatively, a user may select the option “New search” 1028 from thefield value contextual menu 1020 to create a new data search based onthe emphasized field-value pair 1018, and update the search command 804in the search bar 802 to replace the search command in the search barwith search criteria of the emphasized field-value pair (similar to theexample of the keyword that represents the highlighted segment added asthe only search command 804 in the search bar 802 as shown in FIG. 8G).The search system can then perform the new data search based on theupdated search command 804 to determine additional events that eachinclude the search criteria of the emphasized field-value pair, anddisplay an updated search result set of the events that each include theemphasized field-value pair, such as in the search interface 800 thatlists the events 806.

Example Methods

Example methods 1100 are described with reference to FIGS. 11A-11D inaccordance with one or more embodiments of event segment search drilldown, and example methods 1200 are described with reference to FIGS.12A-12D in accordance with one or more embodiments of field value searchdrill down. Generally, any of the components, modules, methods, andoperations described herein can be implemented using software, firmware,hardware (e.g., fixed logic circuitry), manual processing, or anycombination thereof. Some operations of the example methods may bedescribed in the general context of executable instructions stored oncomputer-readable storage memory that is local and/or remote to acomputer processing system, and implementations can include softwareapplications, programs, functions, and the like. Alternatively or inaddition, any of the functionality described herein can be performed, atleast in part, by one or more hardware logic components, such as, andwithout limitation, Field-programmable Gate Arrays (FPGAs),Application-specific Integrated Circuits (ASICs), Application-specificStandard Products (ASSPs), System-on-a-chip systems (SoCs), ComplexProgrammable Logic Devices (CPLDs), and the like.

Computing devices (to include server devices) can be implemented withvarious components, such as a processing system and memory, and with anynumber and combination of different components as further described withreference to the example device shown in FIG. 13. One or more computingdevices can implement the search system, in hardware and at leastpartially in software, such as executable software instructions (e.g.,computer-executable instructions) that are executable with a processingsystem (e.g., one or more computer processors) implemented by the one ormore computing devices. The search system can be stored oncomputer-readable, non-volatile storage memory, such as any suitablememory device or electronic data storage implemented by the computingdevices.

FIGS. 11A-11D illustrate example method(s) 1100 of event segment searchdrill down, which may be implemented by a computing device, adistributed system of computing devices, and/or by one or more userclient devices. The order in which a method is described is not intendedto be construed as a limitation, and any number or combination of themethod operations and/or methods can be performed in any order toimplement a method, or an alternate method.

At 1102, a segment is emphasized in event raw data of an event that isone of multiple events returned as a search result set displayed in asearch interface. For example, the segment 824 in the event raw data 812of the event 810 is emphasized (e.g., highlighted) in the searchinterface 800 (FIG. 8A). The segment is emphasized when a pointer thatis displayed in the search interface 800 moves over the segment, such asto highlight the segment with rollover (e.g., detected when a pointermoves over a segment). For example, a user may move a mouse pointer overthe segment, which is then displayed as the highlighted segment 824.Alternatively, a user can emphasize a segment in the event raw data 812by initiating a selection of the segment, such as with a computer mouse,stylus, or other input device.

At 1104, an input associated with the emphasized segment in the eventraw data is received and, at 1106, a contextual search menu is displayedwith search options that are selectable to operate on the emphasizedsegment in the event raw data. For example, the highlighted segment isselected in response to a user input, such as with a mouse click ortouch input to select the highlighted segment. The contextual searchmenu 836 (FIG. 8C) is then displayed with the search options 838responsive to the received user input, and the contextual search menu836 is displayed proximate the highlighted segment 824 in the searchinterface 800. The search options 838 displayed in the contextual searchmenu 836 include the option “Add to search” 840 that a user can selectto add the highlighted segment 824 as a new keyword to the searchcommand 804 in the search bar 802; the option “Exclude from search” 842that the user can select to exclude the keyword that represents thehighlighted segment 824 from searches; and an option “New search” 844that the user can select to create a new data search based on thehighlighted segment 824 (e.g., replacing the search command 804 in thesearch bar 802 with the keyword that represents the highlighted segment824). Additionally, the contextual search menu 836 of the search options838 includes selectable interface links 846, each associated with acorresponding search option 838, and a selectable interface link 846 isselectable to initiate a new search interface.

At 1108, a selection of one of the search options displayed in thecontextual search menu is received and, at 1110, a search command isupdated in a search bar of the search interface based on the searchoption that is selected from the contextual search menu for thehighlighted segment. For example, a user selection of one of the searchoptions 838 in the contextual search menu 836 is received, and thesearch command 804 in the search bar 802 is updated based on the searchoption that is selected for the highlighted segment 824. These featuresare further described with reference to FIGS. 11B-11D

FIG. 11B illustrates an example method of event segment search drilldown, and is generally described with reference to adding a highlightedsegment as a new keyword to a data search.

At 1112, the selection of a search option is received to add theemphasized segment as a keyword to a data search and, at 1114, thesearch command in the search bar is updated to include the keyword thatrepresents the emphasized segment. For example, a user selects theoption “Add to search” 840 from the contextual search menu 836 (FIG. 8C)to add the highlighted segment 824 as a keyword to a data search, andupdate the search command 804 in the search bar 802 to include thekeyword that represents the highlighted segment, which is shown added tothe search command 804 (FIG. 8D).

At 1116, the data search is performed based on the updated searchcommand to determine the multiple events that each include the keywordthat represents the emphasized segment. Additionally, at 1118, anupdated search result set of the multiple events that each include theemphasized segment is displayed in the search interface. For example,the search system performs the data search based on the updated searchcommand 804 to determine the multiple events 806 that each include thekeyword that represents the highlighted segment, and an updated searchresult set of the events 806 that each include the highlighted segmentis displayed in the search interface 800 (FIG. 8D).

At 1120, an input is received that is associated with the emphasizedsegment in the event raw data of one of the multiple events displayed aspart of the updated search result set. Additionally, at 1122, anadditional search menu of options is displayed. For example, a userselects the highlighted segment 824 (or any of the similar highlightedsegments 848), such as with a mouse click or touch input, and anadditional search menu 850 is displayed proximate the highlightedsegment 824 in the search interface 800 responsive to the user input(FIG. 8E). The additional search menu 850 includes search options 852that are selectable to operate on the highlighted segment 824 in theevent raw data 812 of the displayed event 810. For example, the searchoptions 852 include: an option “Remove from search” 854 that a user canselect to remove the highlighted segment 824 from a search; and anoption “New search” 856 that the user can select to create a new datasearch based on the highlighted segment 824 (e.g., replacing the searchcommand 804 in the search bar with the keyword that represents thehighlighted segment 824).

FIG. 11C illustrates an example method of event segment search drilldown, and is generally described with reference to excluding ahighlighted segment from a data search.

At 1124, the selection of a search option is received to exclude thekeyword that represents the emphasized segment from a data search and,at 1126, the search command in the search bar is updated to exclude thekeyword that represents the emphasized segment. For example, a userselects the option “Exclude from search” 842 from the contextual searchmenu 836 (FIG. 8C) to exclude a keyword that represents the highlightedsegment 824 from a data search, and the search command 804 in the searchbar 802 is updated to indicate a keyword that represents the highlightedsegment is excluded, such as with a “NOT” operator (FIG. 8F).

At 1128, the data search is performed based on the updated searchcommand to determine the multiple events that do not include the keywordthat represents the emphasized segment. Additionally, at 1130, anupdated search result set of the multiple events that do not include theemphasized segment is displayed. For example, the search system performsthe data search based on the updated search command 804 to determine themultiple events 806 that do not include the keyword that represents thehighlighted segment, and an updated search result set of the events 806that do not include the highlighted segment is displayed in the searchinterface 800 (FIG. 8F).

FIG. 11D illustrates an example method of event segment search drilldown, and is generally described with reference to creating a new datasearch based on a highlighted segment.

At 1132, the selection of a search option is received to create a newdata search based on the emphasized segment and, at 1134, the searchcommand in the search bar is updated to include only the keyword thatrepresents the emphasized segment. For example, a user selects theoption “New search” 844 from the contextual search menu 836 (FIG. 8C) tocreate a new data search based on the highlighted segment 824, and thesearch command 804 in the search bar 802 is updated to include only thekeyword that represents the highlighted segment (FIG. 8G).

At 1136, the new data search is performed based on the updated searchcommand to determine the multiple events that include the keyword thatrepresents the emphasized segment. Additionally, at 1138, an updatedsearch result set of the multiple events that include the emphasizedsegment is displayed. For example, the search system performs the newdata search based on the updated search command 804 to determine themultiple events 806 that each include the keyword that represents thehighlighted segment, and an updated search result set of the events 806that each include the highlighted segment is displayed in the searchinterface 800 (FIG. 8G).

FIGS. 12A-12D illustrate example method(s) 1200 of field value searchdrill down, which may be implemented by a computing device, adistributed system of computing devices, and/or by one or more userclient devices. The order in which a method is described is not intendedto be construed as a limitation, and any number or combination of themethod operations and/or methods can be performed in any order toimplement a method, or an alternate method.

At 1202, a field-value pair is emphasized in an event displayed in asearch interface. For example, the field-value pair 1000 of thefield-value pairs 814 in the event 810 is emphasized (e.g., highlighted)in the search interface 800 (FIG. 10A), such as with a mouse pointermoved over the emphasized field-value pair 1000. For example, a user maymove a mouse pointer over the field-value pair, which is then displayedas the emphasized field-value pair 1000. Alternatively, a user canemphasize a field-value pair in the search interface by initiating aselection of the field-value pair, such as with a computer mouse,stylus, or other input device. In implementations, the search interfaceis the event field-picker interface 828 (FIG. 10B) that displays alisting 830 of multiple field-value pairs of the event. The field-valuepair 1018 is emphasized responsive to detection of an input pointer overthe field-value pair.

At 1204, an input is received that is associated with the emphasizedfield-value pair and, at 1206, a field value contextual menu isdisplayed with search options that are selectable to operate on theemphasized field-value pair of the event. For example, the emphasizedfield-value pair is selected in response to a user input, such as with amouse click or touch input to select the emphasized field-value pair.The field value contextual menu 1002 (FIG. 10A) is displayed with thesearch options 1004 proximate the emphasized field-value pair 1000 inthe search interface 800 responsive to the received input. Similarly,the field value contextual menu 1020 (FIG. 10B) can be displayed withthe search options 1022 proximate the emphasized field-value pair 1018in the event field-picker interface 828. The field value contextual menu1020 includes a first statistical event count 1012 that indicates anumber of multiple events 806 that include the search criteria of theemphasized field-value pair 1000, and includes a second statisticalevent count 1014 that indicates a number of multiple events that excludethe search criteria of the emphasized field-value pair.

The search options displayed in the field value contextual menu includean option “Add to search” 1006 that a user can select to add searchcriteria of the emphasized field-value pair 1000 to the search command804 in the search bar 802; an option “Exclude from search” 1008 that theuser can select to add search criteria of the emphasized field-valuepair 1000 to the search command in the search bar as the search criteriaexcluded from events that do not include the emphasized field-valuepair; and an option “New search” 1010 that the user can select to createa new data search based on the emphasized field-value pair 1000 (e.g.,replacing the search command 804 with the search criteria of theemphasized field-value pair 1000). The field value contextual menu 1002of the search options 1004 also includes selectable interface links1016, each associated with a corresponding search option 1004, and aselectable interface link 1016 is selectable to initiate a new searchinterface.

At 1208, a selection of one of the search options displayed in the fieldvalue contextual menu is received and, at 1210, a search command in asearch bar of the search interface is updated based on the search optionthat is selected from the field value contextual menu for the emphasizedfield-value pair. For example, a user selection of one of the searchoptions 1004 in the field value contextual menu 1002 can be received,and the search command 804 in the search bar 802 is updated based on thesearch option that is selected for the emphasized field-value pair.These features are further described with reference to FIGS. 12B-12D.

FIG. 12B illustrates an example method of field value search drill down,and is generally described with reference to adding search criteria ofan emphasized field-value pair to a data search. A field-value pairsearch returns events that have the emphasized field-value pair, and thevalue of the field for an event matches the selected value. A value fora field is an extraction from a specific location in a event (e.g., thelocation defined by an extraction rule). If a value of an emphasizedfield-value pair appears in an event, but is not the value of the fieldfor that event because it is in a location not extracted by theextraction rule defining the field, that event does not meet the searchcriteria of the emphasized field-value pair.

At 1212, the selection of a search option is received to add searchcriteria of the emphasized field-value pair to a data search, and at1214, the search command in the search bar is updated to include thesearch criteria of the emphasized field-value pair. For example, a userselects the option “Add to search” 1006 from the field value contextualmenu 1002 to add the search criteria of the emphasized field-value pair1000 to a data search, and the search command 804 in the search bar 802is updated to include the search criteria of the emphasized field-valuepair (similar to the example of the highlighted segment being added as akeyword to the search command as shown in FIG. 8D).

At 1216, the data search is performed based on the updated searchcommand to determine additional events that each include the searchcriteria of the emphasized field-value pair. Additionally, at 1218, theadditional events that each include the search criteria of theemphasized field-value pair are displayed in the search interface. Forexample, the search system performs the data search based on the updatedsearch command 804 to determine the multiple events 806 that eachinclude the search criteria of the emphasized field-value pair 1000, andan updated search result set of the events 806 that each include thesearch criteria of the emphasized field-value pair is displayed in thesearch interface 800.

FIG. 12C illustrates an example method of field value search drill down,and is generally described with reference to adding search criteria ofan emphasized field value pair that matches events excluding theemphasized field-value pair.

At 1220, the selection of a search option is received to exclude thesearch criteria of the emphasized field-value pair from a data searchand, at 1222, the search command in the search bar is updated toindicate that the search criteria of the emphasized field-value pair isexcluded. For example, a user selects the option “Exclude from search”1008 from the field value contextual menu 1002 to exclude the searchcriteria of the emphasized field-value pair 1000 from a data search, andupdate the search command 804 in the search bar 802 to indicate that thesearch criteria of the emphasized field-value pair is excluded (similarto the example of the keyword that represents the highlighted segmentshown following the “NOT” operator in the search command as shown inFIG. 8F).

At 1224, the data search is performed based on the updated searchcommand to determine additional events that do not include the searchcriteria of the emphasized field-value pair. Additionally, at 1226, theadditional events that do not include the search criteria of theemphasized field-value pair are displayed. For example, the searchsystem performs the data search based on the updated search command 804to determine the multiple events 806 that do not include the searchcriteria of the emphasized field-value pair 1000, and an updated searchresult set of the events 806 that do not include the search criteria ofthe emphasized field-value pair is displayed in the search interface800.

FIG. 12D illustrates an example method of field value search drill down,and is generally described with reference to creating a new data searchbased on an emphasized field-value pair.

At 1228, the selection of a search option is received to create a newdata search based on the emphasized field-value pair and, at 1230, thesearch command in the search bar is replaced with the search criteria ofthe emphasized field-value pair. For example, a user selects the option“New search” 1010 from the field value contextual menu 1002 to create anew data search based on the emphasized field-value pair 1002, and thesearch command 804 in the search bar 802 is replaced with the searchcriteria of the emphasized field-value pair (similar to the example ofthe keyword that represents the highlighted segment replacing the searchcommand 804 in the search bar 802 as shown in FIG. 8G).

At 1232, the new data search is performed based on the updated searchcommand to determine additional events that include the search criteriaof the emphasized field-value pair. Additionally, at 1234, theadditional events that include the search criteria of the emphasizedfield-value pair are displayed. For example, the search system performsthe new data search based on the updated search command 804 to determinethe multiple events 806 that each include the search criteria of theemphasized field-value pair 1000, and an updated search result set ofthe events 806 that each include the search criteria of the emphasizedfield-value pair is displayed in the search interface 800.

Example System and Device

FIG. 13 illustrates an example system generally at 1300 that includes anexample computing device 1302 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe search interface module 1304 that is representative of functionalityto interact with a search service 1306, e.g., to specify and managesearches using a late-binding schema and events as described above andthus may correspond to the client application module 106 and system 100of FIG. 1. The computing device 1302 may be, for example, a server of aservice provider, a device associated with a client (e.g., a clientdevice), an on-chip system, and/or any other suitable computing deviceor computing system.

The example computing device 1302 as illustrated includes a processingsystem 1308, one or more computer-readable media 1310, and one or moreI/O interface 1312 that are communicatively coupled, one to another.Although not shown, the computing device 1302 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 1308 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1308 is illustrated as including hardware element 1314 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1314 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 1310 is illustrated as includingmemory/storage 1316. The memory/storage 1316 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1316 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1316 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 1310 may be configured in a variety of otherways as further described below.

Input/output interface(s) 1312 are representative of functionality toallow a user to enter commands and information to computing device 1302,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1302 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1302. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 1302, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1314 and computer-readablemedia 1310 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1314. The computing device 1302 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1302 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1314 of the processing system 1308. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1302 and/or processing systems1308) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1302 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1318 via a platform 1320 as describedbelow.

The cloud 1318 includes and/or is representative of a platform 1320 forresources 1322. The platform 1320 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1318. Theresources 1322 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1302. Resources 1322 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 1320 may abstract resources and functions to connect thecomputing device 1302 with other computing devices. The platform 1320may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources1322 that are implemented via the platform 1320. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 1300. Forexample, the functionality may be implemented in part on the computingdevice 1302 as well as via the platform 1320 that abstracts thefunctionality of the cloud 1318.

Although embodiments of field value search drill down have beendescribed in language specific to features and/or methods, the appendedclaims are not necessarily limited to the specific features or methodsdescribed. Rather, the specific features and methods are disclosed asexample implementations of field value search drill down, and otherequivalent features and methods are intended to be within the scope ofthe appended claims. Further, various different embodiments aredescribed and it is to be appreciated that each described embodiment canbe implemented independently or in connection with one or more otherdescribed embodiments.

The invention claimed is: 1-30. (canceled)
 31. A computer-implementedmethod, comprising: receiving, at a computing device, first inputcorresponding to selection of a portion of a field-value pair of anevent of a set of events, the field-value pair including a field nameand a value for the field, wherein the set of events is displayed in asearch interface; causing, in response to the first input, display ofselectable search options that can operate on the field-value pair;receiving second input corresponding to selection of a search option ofthe selectable search options; and causing, in response to the secondinput, display of a search command that is based on the search optionand each of the field or the value.
 32. The method of claim 31, whereinthe first input is received through an event field-picker interfacedisplaying a list of field-value pairs of the event.
 33. The method asof claim 31, further comprising: determining that a location associatedwith an input device is proximate to the field-value pair in the searchinterface; and causing emphasis of the first field-value pair.
 34. Themethod of claim 31, wherein, in response to the second input, the searchcommand includes the field or the value.
 35. The method of claim 31,wherein the selectable search options are displayed within a menu thatis proximate the field-value pair within the search interface.
 36. Themethod of claim 31, wherein the selectable search options include an addto search option, an exclude from search option, or a new search option.37. The method of claim 31, wherein the selectable search optionsinclude an add to search option that is selectable to automatically addtext defining search criteria to the search command.
 38. The method ofclaim 31, wherein the selectable search options include an exclude fromsearch option that is selectable to automatically add text definingsearch criteria to the search command.
 39. The method of claim 31,wherein the selectable search options include a new search option thatis selectable to automatically create a new data search based on thefirst field-value pair.
 40. The method of claim 31, wherein the displayof the selectable search options further includes an indication of anumber of events of the set of events that include the first field-valuepair.
 41. The method of claim 31, wherein the display of the selectablesearch options further includes an indication of a number of events ofthe set of events that exclude the first field-value pair.
 42. Themethod of claim 31, wherein, in response to the second input, the searchcommand is modified to require the field or the value.
 43. The method ofin claim 31, further comprising: executing the search command, whereinexecution of the search command identifies a second set of events; andcausing display, within the search interface, of the second set ofevents.
 44. The method of claim 31, further comprising: executing thesearch command, wherein execution of the search command identifies asubset from the set of events, the subset of events excluding the fieldor the value and excluding events from the set of events that includethe field or the value; and causing display, within the searchinterface, of the subset set of events.
 45. The method as recited inclaim 31, wherein the search command is further not based on a searchcommand that generated the set of events.
 46. The method of claim 31,wherein the selectable search options are further displayed withselectable interface links each associated with a particular searchoption of the selectable search options, and wherein a selectableinterface link, when activated, initiates a new search interface. 47.The method of claim 31, further comprising: causing highlighting of thefield-value pair.
 48. The method of claim 31, wherein the set of eventsare returned as a search result, the set of events being identified fromcollected data that comprises at least one of raw data, machine data,performance data, log data, diagnostic information, transformed data, ormashup data combined from multiple sources.
 49. The method of claim 31,wherein the set of events are returned as a search result performedusing a late-binding schema on data collected from one or more sources.50. The method of claim 31, wherein the event comprises a portion of rawdata that is associated with a timestamp indicating a respective pointin time associated with the event.
 51. The method of claim 31, wherein,in response to the second input, the search command is modified torequire the field or the value.
 52. The method of claim 31, wherein, inresponse to the second input, the search command is modified to excludethe field or the value.
 53. The method of claim 31, further comprising:executing the search command, wherein execution of the search commandidentifies a subset of events from the set of events, the subset ofevents including the field or the value and excluding events from theset of events that do not include the field or the value; causingdisplay, within the search interface, of the subset of events.
 54. Acomputer-implemented system, comprising: one or more processors; and oneor more computer-readable media storing instructions thereon that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: receiving, at a computing device,first input corresponding to selection of a portion of a field-valuepair of an event of a set of events, the field-value pair including afield name and a value for the field, wherein the set of events isdisplayed in a search interface; causing, in response to the firstinput, display of selectable search options that can operate on thefield-value pair; receiving second input corresponding to selection of asearch option of the selectable search options; and causing, in responseto the second input, display of a search command that is based on thesearch option and each of the field or the value.
 55. Thecomputer-implemented system of claim 54, wherein the first input isreceived through an event field-picker interface displaying a list offield-value pairs of the event.
 56. The computer-implemented system ofclaim 54, the operations further comprise: determining that a locationassociated with an input device is proximate to the field-value pair inthe search interface; and causing emphasis of the first field-value pair57. The computer-implemented system of claim 54, wherein, in response tothe second input, the search command includes the field or the value.58. One or more computer-readable, non-volatile storage memorycomprising stored instructions that are executable and, responsive toexecution by a computing device, cause the computing device to performactions comprising: receiving, at a computing device, first inputcorresponding to selection of a portion of a field-value pair of anevent of a set of events, the field-value pair including a field nameand a value for the field, wherein the set of events is displayed in asearch interface; causing, in response to the first input, display ofselectable search options that can operate on the field-value pair;receiving second input corresponding to selection of a search option ofthe selectable search options; and causing, in response to the secondinput, display of a search command that is based on the search optionand each of the field or the value.
 59. The one or morecomputer-readable, non-volatile storage memory of claim 58, wherein theselectable search options are displayed within a menu that is proximatethe field-value pair within the search interface.
 60. The one or morecomputer-readable, non-volatile storage memory of claim 58, wherein theselectable search options include an add to search option, an excludefrom search option, or a new search option.